Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction

Shi, Zhenguo; Zhang, Andrew; Xu, Richard Yi Da; Cheng, Qingqing

doi:10.1109/icc.2019.8761445

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…Recurrent neural network (RNN) is one of the deepest network architectures that can memorize arbitrary-length sequences of input patterns. The unique advantage of RNN is that it enables multiple inputs and multiple outputs, which makes it very effective for time sequence data, such as video [71] and CSI [5], [72], [73]. Its principle is to create internal memory to store historical patterns, which are trained via backpropagation through time [74].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang¹,

Chen²,

Wang³

et al. 2022

Preprint

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WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we highlight the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/CHENXINYANsg/WiFi-CSI-Sensing-Benchmark.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang¹,

Chen²,

Wang³

et al. 2022

Preprint

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“…HAR through CSI data from COTS WiFi devices was first studied in, e.g., E-eyes [11], CARM [12]. More recently, several articles showed the effectiveness of machine learning techniques in building algorithms that distinguish human activities based on CSI features [13]- [19]. However, these works do not focus on the robustness to environmental changes and on the generalization capability to previously unseen environments and subjects, which are key enablers for the successful development of WiFi-based sensing systems [9].…”

Section: A Csi Based Human Activity Recognitionmentioning

confidence: 99%

SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points

Meneghello,

Garlisi,

Fabbro

et al. 2021

Preprint

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Here, we propose an original approach for human activity recognition (HAR) with commercial IEEE 802.11ac (WiFi) devices, which generalizes across different persons, days and environments. To achieve this, we devise a technique to extract, clean and process the received phases from the channel frequency response (CFR) of the WiFi channel, obtaining an estimate of the Doppler shift at the receiver of the communication link. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment specific) static objects. The proposed HAR framework is trained on data collected as a person performs four different activities and is tested on unseen setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst case scenario, the proposed HAR technique reaches an average accuracy higher than 95%, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent fashion.

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“…As a result, the researchers concluded that users should move frequency-domain spectral measurements for detection. A gesture recognition system was proposed by Tian et al [37] based on CSI signals. The main concept is to create a virtual antenna using the signals reflected by hand motions.…”

Section: B Csi Based Technologymentioning

confidence: 99%

CSI-IANet: An Inception Attention Network for Human-Human Interaction Recognition Based on CSI Signal

Rahman

Shin

2021

IEEE Access

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In recent years, Wi-Fi infrastructures have become ubiquitous, providing device-free passivesensing features. Wi-Fi signals can be affected by their reflection, refraction, and absorption by moving objects in their path. The channel state information (CSI), a signal property indicator, of the Wi-Fi signal can be analyzed for human activity recognition (HAR). Deep learning-based HAR models can enhance performance and accuracy without sacrificing computational efficiency. However, to save computational power, an inception network, which uses a variety of techniques to boost speed and accuracy, can be adopted. In contrast, the concept of spatial attention can be applied to obtain refined features. In this paper, we propose a human-human interaction (HHI) classifier, CSI-IANet, which uses a modified inception CNN with a spatial-attention mechanism. The CSI-IANet consists of three steps: i) data processing, ii) feature extraction, and iii) recognition. The data processing layer first uses the second-order Butterworth low-pass filter to denoise the CSI signal and then segment it before feeding it to the model. The feature extraction layer uses a multilayer modified inception CNN with an attention mechanism that uses spatial attention in an intense structure to extract features from captured CSI signals. Finally, the refined features are exploited by the recognition section to determine HHIs correctly. To validate the performance of the proposed CSI-IANet, a publicly available HHI CSI dataset with a total of 4800 trials of 12 interactions was used. The performance of the proposed model was compared to those of existing state-of-the-art methods. The experimental results show that CSI-IANet achieved an average accuracy of 91.30%, which is better than that of the existing best method by 5%.

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Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction

Cited by 17 publications

References 13 publications

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points

CSI-IANet: An Inception Attention Network for Human-Human Interaction Recognition Based on CSI Signal

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